innovations designed for deeper learning in higher education (237806130)
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7/27/2019 Innovations Designed for Deeper Learning in Higher Education (237806130)
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B u i l d i n g B l o c k s f o r C o l l e g
e C o m p l e t i o n : B l e n d e d L e a r n i n g
|
b y N a n c y M i l l i c h a p a n d K r i s t e n V o g t The Potential 1 | Current Approaches 2 | NGLC-Funded Blended Learning Models 3 | Lessons Learned about Next Generation Learning 4 | Yet to Learn 13
Primer Questions for Future Work 13 | Resources 14 | Acknowledgements 16 | About the Authors 16 | Next Generation Learning Challenges 16
Key Takeaways
> Seven institutions received $1.7 million in funding from the Next Generation Learning Challenges
(NGLC) Building Blocks for College Completion grant program to scale innovations designed to
promote deeper learning and student engagement in higher education to other institutions. The
group reached 9,955 students at 135 institutions during the grant term.
> Lacking a common definition of “deeper learning,” the grantees adopted different technology-
enabled educational innovations in their efforts to help students achieve it, including
supplementing existing courses, supporting the adoption of blended learning, and completely
redesigning a course (the most successful approach).
> Given the lack of a definition, grantees struggled to find good measures of deeper learning and
developed a range of proxy indicators to understand the effects of their innovations on students.
> Grantee projects covered three to five aspects of the Hewlett Foundation’s definition of deeper
learning (used for this report but not by grantees), with all supporting two aspects, mastery of
core academic content and self-directed learning.
> When implementing the innovations, a majority of instructors assessed deeper learning and used
student data to inform instruction, but less than half used project-based learning, a potential
missed opportunity in postsecondary settings.
> Only one grantee, U-Pace, had statistically significant effects (more positive outcomes for
its students as compared to the control group). U-Pace was also the only grantee that made
substantial holistic changes in faculty practices and classroom pedagogy.
> Results suggest that students need support to transition to the more active role in their own
learning that deeper learning demands and that faculty need support, training, and time to
create, implement, and sustain reforms that change student learning.
> The results also mirror the project evaluator’s findings across all 29 of the NGLC-funded Building
Blocks projects; innovations involving supplemental resources were less successful than whole
course redesign efforts. Fundamental, comprehensive redesign occurring over more than one
academic term appears to promise the best outcomes for deeper learning.
Innovations Designedfor Deeper Learningin Higher EducationBy Andrea Venezia, Associate Director
Institute for Higher Education Leadership & Policy
California State University, Sacramento
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The Potential: What Is Deeper Learning 2 | NGLC’s Deeper Learning Challenge 4 | NGLC-Funded Innovations 4 | Deeper Learning Approaches 10
Challenges to Supporting Deeper Learning through Technology 13 | Implementation and Scaling 15 | Evidence of Learning and Course Completion 16
Conclusion 17 | Resources 18
Building Blocks for College Completion: Blended Learning
The Potential 1 | Current Approaches 2 | NGLC-Funded Blended Learning Models 3 | Lessons Learned about Next Generation Learning 4 | Yet to Learn 13
Primer Questions for Future Work 13 | Resources 14 | Acknowledgements 16 | About the Authors 16 | Next Generation Learning Challenges 16
The Potential:
What Is Deeper Learning? Within higher education, there is an increasing focus
on what and how students learn; historically, these
issues have rarely been discussed in policy and
funding circles, given the deep roots of academic
freedom. Starting in the 1990s, groups such as
the National Center for Public Policy and Higher
Education, through its Measuring Up series, and
other entities began to shine a light on student
learning in postsecondary education—making clear
that we knew surprisingly little about what students
learn and retain. Currently, with the focus on 21st-
century knowledge and skills, workforce readiness,
low completion rates in developmental education and
core gateway classes, and low rates of certificate and
degree completion, there is an increased emphasis on
helping postsecondary institutions examine, define,
and improve student learning.
In addition, there is growing interest in the
nonacademic knowledge and skills required to
succeed in all academic areas. Habits of mind (such
as persistence and self-efficacy) and key cognitive
strategies (such as the ability to hypothesize, analyze,
strategize, and evaluate) are critically importantattributes to foster throughout students’ K–12 and
postsecondary education pathways. In the classroom,
deeper learning is the result of students’ learning core
knowledge along with the utilization of nonacademic
dispositions. It focuses on the interaction between
nonacademic knowledge and skills and the acquisition
of academic content. For its deeper learning grant
making, Next Generation Learning Challenges has
adopted the William and Flora Hewlett Foundation
definition of deeper learning (see also the sidebar).
Innovations Designed for Deeper Learning in Higher Education
Deeper Learning: Knowledge,
Skills, and Beliefs
Mastery of Core Academic Content: Students
build their academic foundation in subjects
such as the humanities, social sciences, math,
and science. They understand key principles
and procedures, recall facts, use the correct
language, and draw on their knowledge to
complete new tasks.
Critical Thinking and Problem Solving:
Students think critically, analytically, and
creatively. They know how to find, evaluate, and
synthesize information to construct arguments.
They can design their own solutions to complex
problems.
Collaboration: Collaborative students work well
in teams. They communicate and understand
multiple points of view and they know how to
cooperate to achieve a shared goal.
Effective Communication: Students
communicate effectively in writing and in oral
presentations. They structure information in
meaningful ways, listen to and give feedback,
and construct messages for particular
audiences.
Self-Directed Learning: Students develop
an ability to direct their own learning. They
set goals, monitor their own progress, and
reflect on their own strengths and areas for
improvement. They learn to see setbacks as
opportunities for feedback and growth. Students
who learn through self-direction are more
adaptive than their peers.
An “Academic Mind-Set:” Students with an
academic mind-set have a strong belief in
themselves. They trust their own abilities andbelieve their hard work will pay off, so they
persist to overcome obstacles. They also learn
from and support each other. They see the
relevance of their schoolwork to the real world
and their own future success.
Source: The William and Flora Hewlett Foundation,
What Is Deeper Learning?
The Potential: What Is Deeper Learning 2 | NGLC’s Deeper Learning Challenge 4 | NGLC-Funded Innovations 4 | Deeper Learning Approaches 5
Challenges to Supporting Deeper Learning through Technology 9 | Implementation and Scaling 11 | Evidence of Learning and Course Completion 14
Conclusion 15 | Resources 16
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The Potential: What Is Deeper Learning 2 | NGLC’s Deeper Learning Challenge 4 | NGLC-Funded Innovations 4 | Deeper Learning Approaches 10
Challenges to Supporting Deeper Learning through Technology 13 | Implementation and Scaling 15 | Evidence of Learning and Course Completion 16
Conclusion 17 | Resources 18
Deeper learning does not have a one-size-fits-all approach or
model. At its core, deeper learning is about providing opportunities
for students so they can become self-motivated, competent
learners (and view themselves as such) who are able to retain and
apply knowledge in a variety of contexts, work effectively with
others, and exhibit the other dimensions of learning described
here. Students learn academic knowledge while using effective
skills and behaviors, with habits of mind and key cognitive
strategies supporting students’ abilities to learn deeply. This
mutually reinforcing relationship is at the core of sound teaching
and learning, but it has not been explicitly encouraged through
large-scale policy and practice in recent years. Many of these
ideas harken back to American education reformer John Dewey’s
philosophies on engagement, interaction, communication,
experiential education, problem-based learning, and going beyond
academic content to reach one’s full potential (for more, see
Dewey’s profile in the PBS series Only a Teacher ).
Classroom innovations that leverage technology have the potential to support deeper learning
in a number of ways. For example, if students learn core content outside the classroom, through
podcasts or online textbooks, that can allow time for deeper forms of engagement within
the classroom, such as group projects and other forms of applying knowledge. This flipped-
classroom approach is particularly helpful in large, lecture-based classes. Other classroom
innovations incorporate self-paced learning; by providing students with digital course materials,
instructors allow individual students to move at the speed that works best for them, engage
more deeply with material they have mastered, and spend additional time on material that
is new or particularly challenging. Furthermore, classroom innovations that use real-time,instantaneous feedback from digital content can help students address their learning gaps and
can also inform instructors’ decisions about the use of instructional time.
This report presents information about the development, adoption, and scaling of
technology-enabled innovations created by seven postsecondary institutions in NGLC’s
Building Blocks for College Completion grant program. The innovations were designed to
promote and support deeper learning and engagement. This report is geared toward helping
those who are interested in improving deeper learning and engagement through educational
innovations—and often classroom-based innovations—that incorporate technology.
Innovations Designed for Deeper Learning in Higher Education
The Potential: What Is Deeper Learning 2 | NGLC’s Deeper Learning Challenge 4 | NGLC-Funded Innovations 4 | Deeper Learning Approaches 5
Challenges to Supporting Deeper Learning through Technology 9 | Implementation and Scaling 11 | Evidence of Learning and Course Completion 14
Conclusion 15 | Resources 16
Information Sources
Data for this report come from grantees’ applications and other materials submitted to NGLC. In addition,
interviews were conducted with the following representatives from three of the innovations, chosen
because they represent varied approaches: Cynthia Powell from Abilene Christian University’s MEIBL,
Jennifer Spohrer from Bryn Mawr College’s blended learning STEM initiative, and Diane Reddy from the
University of Wisconsin–Milwaukee’s U-Pace. Another source is the unpublished independent evaluation of
grant projects conducted by SRI International and commissioned by the Bill & Melinda Gates Foundation.
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The Potential: What Is Deeper Learning 2 | NGLC’s Deeper Learning Challenge 4 | NGLC-Funded Innovations 4 | Deeper Learning Approaches 10
Challenges to Supporting Deeper Learning through Technology 13 | Implementation and Scaling 15 | Evidence of Learning and Course Completion 16
Conclusion 17 | Resources 18
Innovations Designed for Deeper Learning in Higher Education
NGLC’s Deeper Learning Challenge
The first wave of NGLC grants focused on improving postsecondary completion rates by addressing theeffectiveness and quality of the courses that most often are barriers for low-income college students. The NGLC
Building Blocks for College Completion RFP, which was released in October 2010, stated that the main objectives
were to improve “course completion, persistence, and college completion through sustainable, broad-scale,
technology-enabled product, project, or service-based solutions.” The main goal of the grant program was to scale
innovations working at one postsecondary institution to additional institutions. In addition, the RFP included the
following four broad areas of focus, or challenge areas:
> Blended learning models combining face-to-face and online learning activities
> Interactive applications that enhance student engagement and promote deeper learning
> Learning analytics that make current learning performance information available to learners, instructors,
and advisors
> High-quality open core courseware for high-enrollment developmental and introductory courses
There were over 600 applicants (261 in the deeper learning category), of which 29 were funded. Although many
of the 29 grantees included deeper learning in their work, this report provides information on the 7 grantees that
applied directly to the deeper learning challenge area. Unless otherwise cited, all of the outcomes data in this
report are from findings in the unpublished Next Generation Learning Challenges Wave I: Final Evaluation Report
produced by SRI International (SRI), the evaluator for Building Blocks for College Completion.
This report briefly describes each innovation and related available evidence, explains how the Hewlett Foundation
dimensions of deeper learning are addressed by the collection of projects, discusses challenges encountered by
the grantees, summarizes issues related to implementation and scaling, and synthesizes the learnings.
NGLC-Funded Innovations
Table 1 provides the institution’s NGLC website and objective/description for each innovation. The goals of the
innovations vary from allowing students to learn core content outside class to free up class time for a deeper level
of communication and engagement to an online simulation program that promotes experimentation with different
pedagogical techniques. These seven grantees were selected by the NGLC Executive Committee using criteria that
focused on relevance to deeper learning, boldness of innovation, early signals of effectiveness, capacity to execute
successfully, and the perceived promise of the applicants’ scale-up plan.
The Potential: What Is Deeper Learning 2 | NGLC’s Deeper Learning Challenge 4 | NGLC-Funded Innovations 4 | Deeper Learning Approaches 5
Challenges to Supporting Deeper Learning through Technology 9 | Implementation and Scaling 11 | Evidence of Learning and Course Completion 14
Conclusion 15 | Resources 16
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The Potential: What Is Deeper Learning 2 | NGLC’s Deeper Learning Challenge 4 | NGLC-Funded Innovations 4 | Deeper Learning Approaches 10
Challenges to Supporting Deeper Learning through Technology 13 | Implementation and Scaling 15 | Evidence of Learning and Course Completion 16
Conclusion 17 | Resources 18
Innovations Designed for Deeper Learning in Higher Education
Table 1. The deeper learning projects
Innovation Objective/Description
MEIBL
Abilene Christian
University
To make inquiry-based science laboratory activities more accessible to students
by providing support at the moment of need on mobile devices via faculty-
produced video and other visual resources
IPAL Eckerd College
Polling tool to promote student engagement
U-Pace
University of Wisconsin–
Milwaukee
Integrates academics and supports so that students can receive high touch
interventions
BioBook
Wake Forest University
Online biology book that presents small chunks of material in nonlinear ways;
students can learn the topics in any order they choose
simSchool
Association for the
Advancement of
Computing in Education
Classroom simulation for students in teacher education programs; students
experiment with different teaching approaches and learn the possible effects
on interaction and learning
Wayang Outpost
University of
Massachusetts, Amherst
An online adaptive tutor that changes its responses in order to meet a
student’s learning needs
Blended Learning in STEM
in Liberal Arts Institutions
Bryn Mawr College
Determine whether blended learning can improve learning outcomes,
persistence, and postsecondary completion at liberal arts colleges, particularly
in STEM fields
The Potential: What Is Deeper Learning 2 | NGLC’s Deeper Learning Challenge 4 | NGLC-Funded Innovations 4 | Deeper Learning Approaches 5
Challenges to Supporting Deeper Learning through Technology 9 | Implementation and Scaling 11 | Evidence of Learning and Course Completion 14
Conclusion 15 | Resources 16
Deeper Learning Approaches
Each grantee devised different innovations in its attempt to achieve deeper learning. Five of the seven grantees
(AACE, Abilene Christian, Eckerd, UMass, and Wake Forest) developed innovations to supplement existing courses,
usually with the intent of encouraging a more student-centered instructional approach or introducing a new type
of pedagogy. As the SRI report found, “Some of these resources were subject specific (such as Wayang Outpost formath or simSchool for teacher training), and some were more general (such as IPAL, which supports in-class polling
capabilities for a variety of course subjects). Typically, adopting instructors and students could use these resources
as they saw fit to enhance their existing courses.” Overall, across all of the Building Blocks grantees, based on SRI’s
findings, innovations involving supplemental resources were less successful than whole course redesign efforts; the
latter were more prescriptive about instructional approaches.
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The Potential: What Is Deeper Learning 2 | NGLC’s Deeper Learning Challenge 4 | NGLC-Funded Innovations 4 | Deeper Learning Approaches 10
Challenges to Supporting Deeper Learning through Technology 13 | Implementation and Scaling 15 | Evidence of Learning and Course Completion 16
Conclusion 17 | Resources 18
Innovations Designed for Deeper Learning in Higher Education
Abilene Christian University
ACU is a small Christian university that has encouraged
its students to use mobile devices for several years. ACU
requires that all entering students own an iPad. ACU’s
MEIBL (mobile-enhanced inquiry-based learning) was
developed by a chemistry professor to allow students
to watch faculty-produced videos on their computers,
smartphones, and other technologies to help them during
lab. The videos replace some of the short introductory
lectures at the beginning of a lab period, but more
importantly provide support for the students during lab as
an “always available tutor” during the lab activity. MEIBLrequires that faculty flip the class; prior to class, students
explore a lab activity, review instructional podcasts, and
complete a writing exercise related to the preclass work.
Watching the videos helps students prepare more
effectively before they come to class. An advantage of the
MEIBL approach is the availability of the video resources
during lab, so when students are beginning to work with
a new procedure, technique, or piece of equipment, they
can pull up the explanation video showing the exact piece
of equipment, procedure, or technique they will use.
They watch the video side-by-side with their experiment
in the lab to see precisely what they should be doing. In
class, students work in teams to investigate questions,
develop ideas and hypotheses, test hypotheses, generate
and analyze empirically based information, and discuss
findings. An assumption behind MEIBL is that it will lead
to greater student engagement, which, in turn, will lead to
better understanding of concepts, improved retention of
learned material, and better application of information in a
variety of contexts.
Evidence. ACU enrolls approximately 200 students in MEIBL
courses (General Chemistry I and II). California University
of Pennsylvania and Del Mar College in Texas are currentlyusing MEIBL, enrolling approximately 600 and 24 students,
respectively, in MEIBL-enabled science courses. During the
grant period, MEIBL reached 375 students (180 proposed)
at the three institutions. In terms of student outcomes,
93% completed the course, 81% demonstrated subject
matter mastery, 60% demonstrated evidence of deeper
learning, and 91% persisted to the next academic term.
The Potential: What Is Deeper Learning 2 | NGLC’s Deeper Learning Challenge 4 | NGLC-Funded Innovations 4 | Deeper Learning Approaches 5
Challenges to Supporting Deeper Learning through Technology 9 | Implementation and Scaling 11 | Evidence of Learning and Course Completion 14
Conclusion 15 | Resources 16
Furthermore, the NGLC RFP encouraged prospective
grantees to define deeper learning for themselves, in
their own contexts and based on their own needs. For
example, Bryn Mawr staff and faculty used the concept
of intrinsic versus extrinsic motivation as the foundation
for their work to develop blended courses, emphasizing
mastery of concepts and skills in order to foster
intrinsically motivated learning, which tends to correlate
with deeper learning (see the sidebar on SOLO).
This report examines the innovations using Hewlett
Foundation’s definition of deeper learning as a reference
point (see the sidebar), even though it did not function as
such for the grantees. The specific components, however,
map fairly well to many of the grantees’ innovations, as
detailed in table 2 and exemplified in this list:
> Mastery of core content can be seen in BioBook since
it is essentially a biology textbook.
> Critical thinking and problem solving are enabled by
applications such as simSchool, which allows students
to practice complex skills online so they can engage in
higher-order thinking required for more challenging
activities during class time. Engagement and problem
solving are foci of IPAL via its in-class polling.
> Collaboration can be supported by technologies that
open up the space for that to occur during class
(MEIBL) and those that are a platform for group
exercises (IPAL).
> Effective communication is exemplified by simSchool,
which focuses students’ attention on the interactions
between an avatar teacher and a group of students.
> Self-directed learning is a key aspect of Wayang
Outpost, since its online tutoring and information
is calibrated with what each student knows in that
moment in time.
> The development of an academic mind-set is
supported through U-Pace’s Amplified Assistance
and through Wayang Outpost’s interactive tutoring
platform.
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The Potential: What Is Deeper Learning 2 | NGLC’s Deeper Learning Challenge 4 | NGLC-Funded Innovations 4 | Deeper Learning Approaches 10
Challenges to Supporting Deeper Learning through Technology 13 | Implementation and Scaling 15 | Evidence of Learning and Course Completion 16
Conclusion 17 | Resources 18
Innovations Designed for Deeper Learning in Higher Education
Eckerd College
Eckerd College developed IPAL (In-class Polling for
All Learners) as a free, open-source polling tool with
ready-to-use, peer-reviewed questions. An IPAL app
also turns students’ phones into clickers. Students can
use any computer, smartphone, or other web-enabled
device to respond to the polling questions. After polling
the class, the instructor can view and display responses
arrayed in graphic forms. Analyses of student responses
can allow for early identification of students who are
struggling in class, which can lead to additional supportsand increased retention. In addition to helping students
persist, another objective of IPAL is to help students
become more engaged and active learners.
Evidence. In terms of student outcomes, 93% completed
the course, 83% demonstrated subject matter mastery,
83% demonstrated evidence of deeper learning, and
96% persisted to the next academic term. In terms of
scale, Eckerd did not meet its goals for student reach
(4,000 proposed, 531 reached) or institutional reach (50
proposed, 5 reached).
The Potential: What Is Deeper Learning 2 | NGLC’s Deeper Learning Challenge 4 | NGLC-Funded Innovations 4 | Deeper Learning Approaches 5
Challenges to Supporting Deeper Learning through Technology 9 | Implementation and Scaling 11 | Evidence of Learning and Course Completion 14
Conclusion 15 | Resources 16
NGLC expected the classroom innovations it funded
to contribute to deeper learning through increased
engagement, but not necessarily to touch each dimension
of deeper learning as described in the Hewlett Foundation
definition. Examining the innovations according to
each dimension of the definition can help illuminate
the strengths and weaknesses of the different tools
and strategies the grantees developed, relative to the
outcomes they were seeking.
While there is great variation in how the seven grantees
approached deeper learning, there are some clear
overlaps. Most, for example, view deeper learning as
going beyond learning a skill, or some particular piece
of knowledge, and applying it intensively. Mastery of
core academic content (six grantees) and self-directed
learning (all grantees) are the two areas that most or
all grantees focused on (see table 2). The popularity of
these two dimensions in particular is relatively intuitive
because (1) if students learn core content on their own
time through a technology tool, it creates more time for
more engaged in-person learning with groups of peers
or through instructor-led activities, and (2) students
can make choices about what and how they learn—that
is, direct their own learning—through technology such
as an online tutoring program or self-paced curricula.
In terms of mastery of core academic content, this was
reflected, for example, by the development of BioBook,
the supplemental online biology textbook, or Bryn Mawr’s
use of online, interactive tutorials to emphasize mastery
in blended STEM courses. The self-directed learning
component can be seen, for example, in U-Pace’s mastery-
based model and in IPAL, where students take initiative to
fill in their knowledge gaps highlighted by the polling.
The relationships between each grant and the components
of the definition of deeper learning are detailed in table 2.
The SOLO TaxonomyMEIBL developers defined deeper learning by using
a taxonomy called SOLO that provides a process for
faculty to assess students’ work on the basis of its
overall quality rather than on “bits and pieces” of what
students got right on a test. It provides information
for instructors to support student learning from the
memorization of basic facts to the use of extended
abstract understanding.
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The Potential: What Is Deeper Learning 2 | NGLC’s Deeper Learning Challenge 4 | NGLC-Funded Innovations 4 | Deeper Learning Approaches 10
Challenges to Supporting Deeper Learning through Technology 13 | Implementation and Scaling 15 | Evidence of Learning and Course Completion 16
Conclusion 17 | Resources 18
Innovations Designed for Deeper Learning in Higher Education
The Potential: What Is Deeper Learning 2 | NGLC’s Deeper Learning Challenge 4 | NGLC-Funded Innovations 4 | Deeper Learning Approaches 5
Challenges to Supporting Deeper Learning through Technology 9 | Implementation and Scaling 11 | Evidence of Learning and Course Completion 14
Conclusion 15 | Resources 16
Innovations Designed for Deeper Learning in Higher Education
Table 2. Mapping grantees’ projects to Hewlett Foundation’s components of deeper learning*
* “Tech-provided” indicates that the dimension of deeper learning is intended to be achieved through the use of the technology tool alone.
“Tech-enabled” indicates that the dimension of deeper learning may be achieved through the instructional strategies that the use of the technology
tool makes possible (for example, project- or inquiry-based learning opportunities supported by the online delivery of core academic content).
As displayed in table 2, the grantees’ projects cover three to five aspects of deeper learning each, whether directly
through the technology or enabled through another component of the innovation. For example, while almost all of
the grantees intend for core content to be learned through the use of the technology tool alone (tech-provided),
the majority that focus on critical thinking and problem-solving intend for the tool to make learning those skills
happen through the use of other instructional strategies that the technology makes possible (tech-enabled).
MEIBL IPAL U-Pace BioBook simSchool UMass Bryn Mawr
Mastery of CoreTech- Tech- Tech- Tech- Tech- Tech-
Academic provided provided provided provided provided provided
Content
Critical ThinkingTech- Tech- Tech- Tech- Tech- Tech-
and Problemenabled enabled provided enabled provided enabled
Solving
Collaboration Tech- Tech- Tech- Tech- Tech-
enabled enabled provided provided enabled
Effective Tech- Tech-
Communication enabled provided
Self-Directed Tech- Tech- Tech- Tech- Tech- Tech- Tech-
Learning enabled provided provided provided provided provided provided
Development Tech-
of Academic enabled Tech-
Mind-Set (Amplified) provided
Assistance)
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The Potential: What Is Deeper Learning 2 | NGLC’s Deeper Learning Challenge 4 | NGLC-Funded Innovations 4 | Deeper Learning Approaches 10
Challenges to Supporting Deeper Learning through Technology 13 | Implementation and Scaling 15 | Evidence of Learning and Course Completion 16
Conclusion 17 | Resources 18
Innovations Designed for Deeper Learning in Higher Education
The Association for the Advancement
of Computing in Education
AACE co-sponsored the scale-up proposal from simSchool
as a classroom simulation for graduate students in teacher
education programs. By using simSchool, students can examin
classroom management styles, explore instructional strategie
and practice relationship-building techniques with simulated
students. Students use game-like tools and interactive feature
in virtual classrooms to role-play teaching and interacting
with a diverse group of simulated students (diverse in terms offactors such as age, achievement, race/ethnicity, personalities
language proficiency, special needs, and learning styles). The
developers hope that the act of simulation engages students a
deeper levels than do traditional pedagogical methods. Since
simSchool is entirely web based, it does not need to integrate
with any other kind of technology.
Evidence. In terms of scale, AACE exceeded its goals for
both student reach (4,000 proposed, 7,508 reached) and
institutional reach (7 proposed, 116 reached). Outcome data
concerning undergraduate success were not provided in SRI’s
report; however, several outcomes related to teacher educatio
have been reported in the literature including increased
knowledge of teaching, increased self-efficacy as a teacher, an
increased positive attitudes about using game-based methods
in teaching.1
1. Knezek, G., Christensen, R., Tyler-Wood, T., Fisser, P., & Gibson, D. (2012).
“simSchool: Research Outcomes from Simulated Classrooms,” in Proceedings
of the Society for Information Technology & Teacher Education International
Conference 2012, ed. Resta P. (Chesapeake, VA: AACE).
The Potential: What Is Deeper Learning 2 | NGLC’s Deeper Learning Challenge 4 | NGLC-Funded Innovations 4 | Deeper Learning Approaches 5
Challenges to Supporting Deeper Learning through Technology 9 | Implementation and Scaling 11 | Evidence of Learning and Course Completion 14
Conclusion 15 | Resources 16
Challenges to Supporting Deeper
Learning through TechnologyInterviewees cited the following areas as major
challenges.
Measurement difficulties. Grantees struggled to find
good measures of deeper learning and developed a
range of proxy measures to understand the effects of
their innovations on students. Much of what the grantees
wished to learn could not be measured by a standardized
test. As an interviewee from Bryn Mawr stated, “The
hardest thing for us was figuring out how to measure
deeper learning. There is not a lot to go on in the
literature.” They decided to use course grades as a proxy,
supplemented with qualitative analysis of attitudinal
survey data from students and faculty to determine
whether respondents chose language suggesting blended
learning correlated with deep learning—for example,
students discussing impact on their learning in terms
of mastery rather than grades. MEIBL staff conducted
student surveys, administered writing assessments, used
end-of-semester lab reports, and asked students to write
reflections about their learning. They found that students
learned more content; could express what they learned
more deeply; and believed that MEIBL enhanced theirconfidence, ability to learn independently, engagement,
and overall experience in their labs. U-Pace developed
its own quizzes with a high bar, and a high level of
support, for students to pass in order to indicate mastery.
U-Pace staff also used a critical thinking rubric to assess
students’ understandings.
Inadequate capacity and support. Faculty adopting the
grantees’ innovations required both technological and
pedagogical support as well as time, which weren’t always
available at adopting institutions. The technologies,
and the support available to adopt and learn about newtechnologies, vary greatly across institutions. As one
interviewee noted, “It can be exhausting if you’re doing
it [creating and implementing a new technology] on your
own. It can be time-consuming.” In addition, it is likely
not possible for instructors to redesign every course they
teach in one year. Instructors need support to learn what
is achievable in one year, given that instructors cannot
stop teaching, conducting research, or contributing to
service on campus in order to redesign their courses.
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The Potential: What Is Deeper Learning 2 | NGLC’s Deeper Learning Challenge 4 | NGLC-Funded Innovations 4 | Deeper Learning Approaches 10
Challenges to Supporting Deeper Learning through Technology 13 | Implementation and Scaling 15 | Evidence of Learning and Course Completion 16
Conclusion 17 | Resources 18
Building Blocks for College Completion: Blended Learning
One interviewee reported that faculty participating in the pilot were often
discouraged to discover that commercial “off-the-shelf” courseware packages
in their field were not compatible with deeper learning, or that open
educational resources (OER, or free resources that are licensed to be used,
remixed, and repurposed by others) were often technically out-of-date, lacked
mechanisms for enabling faculty to collect and view student learning data for
faculty, and rarely came with tech support. Some faculty members responded
by creating their own materials, but this required even more time and vast
skill building—as one economics professor put it, “I’m a teacher, not a software
designer. I want to focus on my strengths.”
Uneven faculty engagement. Grantees have found it hard to engage new
faculty members. When these types of grant opportunities arise, faculty who
feel comfortable with new technologies, and with experimentation in this
arena, become involved. Reaching faculty who do not volunteer for these
opportunities is very difficult for a variety of reasons, including discomfort or
lack of experience with new technologies, a lack of support to troubleshoot
glitches, and philosophical beliefs about technology in the classroom. The last issue might be particularly relevant
for deeper learning because it connects so completely with pedagogy, with each instructor controlling instructional
philosophies and techniques. There is no formula or particular pedagogy behind deeper learning.
SRI surveyed instructors and found that their motivations to experiment with the grantees’ innovations were
generally student-centered. For example, 33 stated that they volunteered to use the tech-enabled innovation
because they were interested in exploring online teaching and learning (compared with 12 who indicated that they
did not have such an interest). Twenty-eight stated that their motivation was to have greater student engagement
(compared with 17 who were not motivated by that issue). Thirty-three stated that they expected that their
Innovations Designed for Deeper Learning in Higher Education
“The innovations were used
predominantly by instructors
who taught science and
education courses, although
the innovations were also used
in social science and math
courses.”
The Potential: What Is Deeper Learning 2 | NGLC’s Deeper Learning Challenge 4 | NGLC-Funded Innovations 4 | Deeper Learning Approaches 5
Challenges to Supporting Deeper Learning through Technology 9 | Implementation and Scaling 11 | Evidence of Learning and Course Completion 14
Conclusion 15 | Resources 16
Wake Forest University
Science faculty members at Wake Forest examined data about failure and drop-out rates in introductory science
courses and decided to develop an iPad application to improve student success in biology. The application
evolved into an online textbook, BioBook. Working with technology partners at Odigia, they created BioBook
within a Moodle-compatible platform that students can access from any web-enabled device. BioBook is
structured with small chunks of material presented in nonlinear ways, so students can explore topics in any order
they choose. “Personal Progress Maps” are embedded throughout BioBook and track the modules students visit
and complete, the amount of time spent on them, questions they pose electronically, assessments completed,
and the time spent on BioBook tasks. Instructors can use the information from the maps to monitor students’
progress. The theory of action is that collaborative interactions and nonlinear material combine to increasestudent learning and engagement. The developers hope the maps will make students more accountable and
engaged by allowing them to investigate the materials they need.1
Evidence. In terms of student outcomes, 92% completed the course, 70% demonstrated subject matter
mastery, 13% demonstrated evidence of deeper learning, and 85% persisted to the next academic term.
In terms of scale, during the grant period, Wake Forest did not achieve its goal for student reach (4,000
proposed, 504 reached) but did meet its goal of reaching four institutions.
1. See Brett Eaton, “BioBook—eText Evolved,” Wake Forest University News Center, April 8, 2011.
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Challenges to Supporting Deeper Learning through Technology 13 | Implementation and Scaling 15 | Evidence of Learning and Course Completion 16
Conclusion 17 | Resources 18
students would learn more with the innovation (12 did not believe that
to be the case). No faculty members thought that using the innovation
would be easier than current practice, and almost no faculty thought
that the innovation would create a more flexible schedule (one) or be
attractive on a résumé (two).
Variable instructional approaches. Grantees found that some faculty
members implementing their innovation were faithful to the classroom
innovation’s pedagogical model, while others were not, even when
training was provided. As one interviewee explained it, “A lot of what
we do in the classroom is personality dependent.” The interviewee
had trained several faculty members in inquiry-based curricula and related pedagogy. During classroom
observations, she found that while some faculty members were faithful to the model, one was not. She
provided additional training for that faculty member, but could not provide enough, given that the grant was
just for one year. Also, she questioned the utility of trying to assess deeper leaning based on one year-long
effort. An interviewee who led a cross-campus innovation noted, “People across campuses did their own thing.
Some were similar to what our faculty was doing and some were not.”
Implementation and Scaling
As mentioned earlier, the goal of the grant program was to scale innovations working
at one postsecondary institution to additional institutions. In general, SRI’s researchersfound that almost all of the 29 innovations funded by NGLC were not ready to scale
to other institutions at the start of their grant period. Consequently, many of the
implementation experiences the grantees encountered were not related to deeper
learning. Most difficulties centered on technology compatibility, collaboration, and the
development of cross-institutional culture—issues that could be problematic for any
attempt to scale a tech-based initiative across institutions.
According to SRI, the seven grantees reached 9,955 students at 135 institutions
combined. The grant project sidebars detail the numbers of students and institutions
Innovations Designed for Deeper Learning in Higher Education
Instructors’ Perceptions of the Innovations
From a survey of instructors using the seven grantees’ innovations in their courses, 70% reported that
they assessed deeper learning in all of their courses/sections; deeper learning was defined on the survey
as content mastery, problem solving, critical thinking, and/or teamwork. A majority of the instructors
using the innovations reported that they used the following features: assessing deeper learning (87%),
using student data to inform future instruction (79%), and using blended online and classroom-based
activities (60%). Most of the instructors surveyed were satisfied with the innovation (25% were highly
satisfied and 55% were moderately satisfied), but 9% were moderately dissatisfied; 21% reported
their students were highly satisfied with the innovation, 40% reported their students were moderately
satisfied, 9% reported their students were moderately dissatisfied, and 6% reported their students were
highly dissatisfied. The survey was conducted by SRI International.
“The interviewees’ main
implementation and
scaling concerns included
providing mentoring and
support, securing staff
time, and having a unified
scaling approach.”
The Potential: What Is Deeper Learning 2 | NGLC’s Deeper Learning Challenge 4 | NGLC-Funded Innovations 4 | Deeper Learning Approaches 5
Challenges to Supporting Deeper Learning through Technology 9 | Implementation and Scaling 11 | Evidence of Learning and Course Completion 14
Conclusion 15 | Resources 16
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Challenges to Supporting Deeper Learning through Technology 13 | Implementation and Scaling 15 | Evidence of Learning and Course Completion 16
Conclusion 17 | Resources 18
Innovations Designed for Deeper Learning in Higher Education
reached during the grant period. (The totals may
have increased since the grant period concluded.)
The innovations were used predominantly by
instructors who taught science and education
courses, although the innovations were also used in
social science and math courses.
The survey findings that a majority of instructors
were assessing deeper learning and using student
data (see the sidebar, “Instructors’ Perceptions
of the Innovations”) support some of the design
assertions made earlier regarding the deeper
learning approaches taken by the grantees. On the
other hand, almost half of instructors (43%) reported
that project-based learning was not a feature of
the innovation. This may be a missed opportunity
for promoting deeper learning through these
innovations.
Instructors were more likely to indicate that they
did not encounter many implementation challenges,
but did report challenges with technology such as
access, reliability, and ease of use (49%) and student
resistance (26%). Instructors using all 29 grantee
innovations noted these same challenges. According
to SRI, instructors described student resistance as
a reluctance to take responsibility for initiating and
managing their own learning—suggesting that efforts
to promote deeper learning require support for
students to transition to a more active role in their
own learning.
The interviewees’ main implementation and scaling
concerns included providing mentoring and support,
securing staff time, and having a unified scaling
approach. They reported that mentoring increases
the chances of successfully scaling the innovation.
Instructors and staff cannot use a technology
effectively absent the appropriate supports and
time to learn. Having a faculty resource center with
available training and support, along with release
The Potential: What Is Deeper Learning 2 | NGLC’s Deeper Learning Challenge 4 | NGLC-Funded Innovations 4 | Deeper Learning Approaches 5
Challenges to Supporting Deeper Learning through Technology 9 | Implementation and Scaling 11 | Evidence of Learning and Course Completion 14
Conclusion 15 | Resources 16
University of Wisconsin–MilwaukeeUWM developed its U-Pace instructional approach for
Introduction to Psychology, a course that most students
take. U-Pace provides the capacity for students to receive
relatively “high touch” supports within large introductory
courses without a large infusion of funding. U-Pace integrates
academics and supports for all students in courses that have
adopted the model. More specifically, U-Pace combines self-
paced, mastery-based learning with “Amplified Assistance”—
tailored emails from instructors to students, encouraging
students to succeed. The mastery-based component allows
students to move along to new content after they master the
concepts in each module (as evidenced by earning at least
90% on a multiple-choice quiz aligned with the module).
Students can retake quizzes as many times as they wish
without penalty, but they must wait at least one hour before
retaking.
The two components—mastery-based learning and Amplified
Assistance—are intended to work in tandem. An objective of
the two-pronged approach is to give students more control
over their learning; the model’s theory of action is that the
feeling of control will lead to greater learning and academic
success. UWM worked with the Society for the Teaching of
Psychology, which functions as Division 2 of the AmericanPsychological Association, to scale the adoption of U-Pace at
other institutions.
Evidence. Several analyses, some of which included
comparison groups and random assignment, found
that students in U-Pace courses, including academically
underprepared students, earned higher test scores and course
grades than students in traditional courses, even six months
after the course ended. (More information can be found in
an EDUCAUSE Review Online article and in the “Evidence of
Learning and Course Completion” section of this report.) In
terms of student outcomes, 56% completed the course, 46%
demonstrated subject matter mastery, 46% demonstrated
evidence of deeper learning; no data were reported about
persistence to the next academic term.
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Challenges to Supporting Deeper Learning through Technology 13 | Implementation and Scaling 15 | Evidence of Learning and Course Completion 16
Conclusion 17 | Resources 18
Innovations Designed for Deeper Learning in Higher Education
time from teaching a course provided by the college
or university, helps lead instructors scale their
efforts across a campus and to other campuses. For
example, MEIBL focused on providing supports to
faculty and scaling slowly within a course and then
scaling across courses. The faculty involved started
with entry-level courses (general chemistry) and
then moved to organic chemistry and upper-division
biochemistry courses, so that students who continue
in a science major will have reinforcing experiences.
In order to scale effectively, sufficient staff time must
be devoted to the effort. As one interviewee stated,
“It takes an enormous amount of time to create and
implement and sustain and scale work like this; you
basically need a full-time person to do that.” Many
faculty members wish to remain in the classroom
for at least part of their time, and many also have
publication requirements. It is difficult to weigh the
pressures of existing responsibilities against the
time investment new innovations require. Bryn Mawr
faculty reported that it also took a great deal of time
to vet and test different online resources to support
the best blended learning approach for their own
courses.
Furthermore, grantees recommended, based on their
experiences, that developers ensure that applications
are not tethered to particular platforms, given how
quickly platforms change. Web-based applications
can be used by anyone with access to the Internet
and are not subject to revision or obsolescence if
platforms change. This accessibility can help with
scale, as not needing to integrate with existing
platforms can allow for easier widespread adoption.
The Potential: What Is Deeper Learning 2 | NGLC’s Deeper Learning Challenge 4 | NGLC-Funded Innovations 4 | Deeper Learning Approaches 5
Challenges to Supporting Deeper Learning through Technology 9 | Implementation and Scaling 11 | Evidence of Learning and Course Completion 14
Conclusion 15 | Resources 16
University of Massachusetts, AmherstUMass and its partners from Springfield Technical College,
Greenfield Community College, and Holyoke Community
College developed the Math Fundamentals Tutor to Improve
College Completion project in order to (1) promote deeper
learning for students in several mathematics courses who
are at-risk of earning a nonpassing grade/dropping out of the
class and (2) enable students, faculty, and staff to use real-
time analytics to increase student success. The intervention
begins with assessment and placement and continues with
individualized instruction through Wayang Outpost, an
online adaptive tutor that changes its responses in order to
meet a student’s learning needs. Wayang Outpost utilizes
mathematics problems, hints, interactive media, and videos.
It is “smart” software in that it has an “adaptive mechanism
that tailors the sequencing of problems using cutting-edge
research in cognitive science, multiple learning paths, and
embedded assessment.” It uses affective and motivational
information from student logs to tailor nonacademic supports.
The application is used in basic, preparatory, and intermediary
algebra and geometry and in developmental math.
Evidence. UMass found that nearly 3,000 students
in Massachusetts and Arizona using Wayang Outpost
demonstrated significant learning gains, in addition toimproved attitudes, increased motivation, and reduced
frustration and anxiety. In terms of student outcomes, 81%
completed the course, 81% demonstrated subject matter
mastery, 81% demonstrated evidence of deeper learning, and
49% persisted to the next academic term.
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Challenges to Supporting Deeper Learning through Technology 13 | Implementation and Scaling 15 | Evidence of Learning and Course Completion 16
Conclusion 17 | Resources 18
Innovations Designed for Deeper Learning in Higher Education
Evidence of Learning and Course Completion
SRI’s evaluation found that students who participated in efforts funded through the deeper learning challengearea generally had better outcomes than did students in other challenge areas, although none of the differences
are statistically significant. These results, presented in table 3, do not include data from simSchool, which were not
available.
Table 3. Student academic results by challenge area
Effect sizes of the outcomes—the impact of the innovation on student outcomes in relation to a control group
(students in the same course but without the innovation)—may shed more light. SRI estimated the effect sizes of
the six grant projects with outcomes data and statistically significant effects were found only for University of
Wisconsin–Milwaukee’s U-Pace project. The effects were positive, meaning that students in U-Pace classrooms
had better outcomes than students in control-group classrooms. The effect size of 0.621 was the largest effect of
any of the 22 of 29 Building Blocks projects included in the analysis, which is the equivalent of raising the average
student’s score on a 100-point exam from 50 to about 73. U-Pace had significant
and positive effects for both low-income students and their higher-income
peers. None of the other deeper learning projects had significant effects,
meaning outcomes for students in the deeper learning project classrooms
were not statistically different from outcomes for students in control-group
classrooms.
SRI concluded that the majority of the Building Blocks projects did not
require substantial changes in faculty practices and classroom pedagogy as a
whole, except for U-Pace (which moved away from lecture-based instruction
toward mastery-based progression) and one other project (Do the Math! from
Chattanooga State Community College, which was funded by NGLC under the
blended learning challenge area and was not studied for this report). This may
be one of the reasons why significant effects on outcomes were found for
U-Pace but not for the other deeper learning innovations. SRI also reported
that, with regard to U-Pace, “Program leaders and instructors attribute the
success of the program to giving students the opportunity to work through
the course at their own pace and receive faculty feedback, study strategies,
constructive support, and encouragement.”
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Challenges to Supporting Deeper Learning through Technology 9 | Implementation and Scaling 11 | Evidence of Learning and Course Completion 14
Conclusion 15 | Resources 16
“SRI also reported that, with
regard to U-Pace, ‘Program
leaders and instructors
attribute the success of the
program to giving students
the opportunity to work
through the course at their
own pace and receive faculty
feedback, study strategies,
constructive support, and
encouragement.’”
Completion Subject Mastery Deeper Learning Persistence
Challenge Area % N % N % N % N
Deeper learning 85 2,239 74 2,168 56 1,992 83 1,421
Blended learning 75 26,548 60 26,546 41 13,346 67 11,967
Open core83 8,324 67 7,236 49 2,549 66 4,856
courseware
Learner analytics 89 14,347 65 10,326 NA NA 72 4,882
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Challenges to Supporting Deeper Learning through Technology 13 | Implementation and Scaling 15 | Evidence of Learning and Course Completion 16
Conclusion 17 | Resources 18
Innovations Designed for Deeper Learning in Higher Education
Conclusion
Even with the limited data available regarding these innovations’contributions to deeper learning, it is clear that challenges
for students, faculty, and staff must be overcome in order for
technology-enabled innovations to improve student learning.
Instructors found that students need support to transition
into deeper learning in order to be able to take ownership of
their own learning in a manner different from in traditional
classrooms. An abrupt entry into a deeper learning–focused
classroom is not likely to be a successful strategy.
Similarly, faculty members need training and support in order
to create, implement, and sustain reforms that change howand what students learn as fundamentally as deeper learning
intends to do. Faculty and staff developed the innovations
discussed in this report to meet specific needs on their
campuses; these innovations were not developed by information
technology specialists. Faculty and staff networks, interests,
and commitment to supporting teaching and learning are all
hallmarks of this group of projects. Many instructors cannot
redesign their courses, or adopt new technologies, without
significant supports and resources. If instructors do not have the
appropriate training, the models might not be implemented with
fidelity.
Moreover, these efforts call into question the traditional
academic calendar and sequence of courses that often are not
interconnected in a meaningful way. Achieving deeper learning
appears to require more than one course over one academic
term, which implies the need for significant faculty collaboration
to continue certain strategies and pedagogies over the course of
several classes.
U-Pace, the only innovation that showed statistically significant
improvements in student learning, moved away from lectures
and used a mastery-based approach. This finding suggests that
deeper learning may require an even deeper level of pedagogicalredesign. Appending an innovation to a traditionally taught
course within a traditionally organized curriculum may not
generate hoped-for improvements in student outcomes. Given
the nascent nature of both technological and deeper learning
reforms in postsecondary education, these initiatives represent
an important first phase of experimentation. The projects’
efforts provide critically important information for others who
are leading the next phase of efforts to enable deeper learning
and engagement for students in postsecondary education.
The Potential: What Is Deeper Learning 2 | NGLC’s Deeper Learning Challenge 4 | NGLC-Funded Innovations 4 | Deeper Learning Approaches 5
Challenges to Supporting Deeper Learning through Technology 9 | Implementation and Scaling 11 | Evidence of Learning and Course Completion 14
Conclusion 15 | Resources 16
Bryn Mawr CollegeThis project studied whether blended learning could
provide benefits in the more intimate context of
a residential liberal arts college and, in particular,
whether it could improve learning outcomes,
persistence, and postsecondary completion in STEM
fields. In academic year 2011–12, Bryn Mawr faculty
piloted blended approaches in 18 courses, with a
particular focus on introductory STEM courses. For the
online component of these courses, faculty developed
or adopted existing online, interactive lessons, tutorials,
and quizzes, which not only introduced students to
concepts and skills but also enabled them to practice
for course assessments, evaluate their learning, and get
immediate feedback.
Students could work online as much or as little as
they needed to in order to master the concepts and
skills. Faculty members used the data from the online
activities to identify and support struggling students
and adjust in-class instruction and activities. Faculty
also used the online learning component to free up
class time and prepare students for activities designed
to foster deeper learning—such as project-based
learning or more intensive group discussion. Bryn Mawrfaculty shared their experiences with a consortium of
40 liberal arts colleges. Forty faculty members from
25 of those colleges developed and piloted blended
courses for academic year 2012–13, although the timing
of these pilots made it difficult to collect outcome data
from partners during the grant period.
Evidence. Of the 729 students enrolled in Bryn Mawr
College pilot courses, just over 94% of students in
participating courses completed their courses with a
grade of 2.0 or higher, and 98% remained enrolled in
the college the following semester; 14% of the students
in these courses were low income (Pell-grant eligible),
and they completed, reenrolled, and demonstrated
evidence of deeper learning at roughly similar rates
as the population as a whole—94%, 94%, and 36%,
respectively. However, only 70% of low-income
students achieved subject mastery, compared to 86%
of all students.1
1. Next Generation Learning Challenges, Grant Recipients,
Bryn Mawr College.
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Building Blocks for College Completion: Learning Analytics
The Potential: What Is Deeper Learning 2 | NGLC’s Deeper Learning Challenge 4 | NGLC-Funded Innovations 4 | Deeper Learning Approaches 5
Challenges to Supporting Deeper Learning through Technology 9 | Implementation and Scaling 11 | Evidence of Learning and Course Completion 14
Conclusion 15 | Resources 16
Resources
The following websites provide information, materials, and examples of deeper learning in K–12 and higher
education.
> Deeper Learning from the William and Flora Hewlett Foundation
> Deeper Learning from the Alliance for Excellent Education
> Deeper Learning Resources from Deeper-Learning.org
> Deeper Learning Resources from Getting Smart
> Deeper Learning from Next Generation Learning Challenges
These tools and readings support the design, adoption, and measurement of deeper learning.
> Breakthrough Models for College Completion: The Next Generation of Models for Higher Education, a
compilation of nine new postsecondary degree program models awarded grants from NGLC in 2012. These
models are examples of the fundamental, comprehensive redesign that findings from this report suggest
may hold greater promise of promoting deeper learning outcomes than supplemental resources for
individual courses.
> Deeper Learning MOOC, a free, flexible, nine-week course that will allow K–16 educators to learn how deeper
learning can be put into practice.
> Blended Learning in the Liberal Arts Conference, an annual forum for faculty and staff to share findings and
experiences related to using blended learning to improve learning outcomes and support the close faculty-
student relationships and deep, lifelong learning that are the hallmarks of a liberal arts education.
> Assessing Deeper Learning from the Alliance for Excellent Education—a 2011 brief that shows what
assessments measuring deeper learning might look like and how they can be implemented feasibly.
> Deeper Learning: Authentic Student Assessment from Edutopia—an article by Bob Lenz, founder and chief
of innovation for Envision Education, describing three leverage points for developing and implementing a
deeper learning student assessment system.
> Spotlight on Deeper Learning from Education Week—a collection of articles addressing teaching studentswith disabilities, dual-language instruction, creativity, brain and learning sciences, and mastery-based
learning.
> How Digital Learning Contributes to Deeper Learning from Getting Smart—a report by Tom VanderArk and
Carri Schneider identifying personalized skill building, schools and tools, and extended access as three
primary ways that digital learning promotes deeper learning.
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Building Blocks for College Completion: Learning Analytics
The Potential: What Is Deeper Learning 2 | NGLC’s Deeper Learning Challenge 4 | NGLC-Funded Innovations 4 | Deeper Learning Approaches 5
Challenges to Supporting Deeper Learning through Technology 9 | Implementation and Scaling 11 | Evidence of Learning and Course Completion 14
Conclusion 15 | Resources 16
Resources (continued)
Publications and presentations from NGLC grant recipients provide further information about the innovations.
Abilene Christian University, MEIBL
> Powell, Cynthia B., Scott Perkins, Scott Hamm, Rob Hatherill, Louise Nicholson, and Dwayne Harapnuik.
“Mobile-Enhanced Inquiry-Based Learning: A Collaborative Study.” EDUCAUSE Quarterly (December 15,
2011).
Eckerd College, IPAL
> Junkin, Bill. “Beyond Clickers: Increasing Student Learning Through In-Class Polling Using Web-Enabled
Devices.” NGLC (blog). July 10, 2013.
University of Wisconsin–Milwaukee, U-Pace
> Barth, Dylan, Raymond Fleming, Laura Pedrick, and Diane M. Reddy, “Understanding the Impact of
Online Instruction: Strategies and Lessons from the U-Pace Instructional Approach.” Poster presented at
EDUCAUSE 2013, Anaheim, California, October 15–18, 2013.
> Reddy, Diane M., Raymond Fleming, Laura E. Pedrick, Danielle L. Jirovec, Heidi M. Pfeiffer, Katie A. Ports,
Jessica L. Barnack-Tavlaris, Alicia M. Helion, and Rodney A. Swain. “U-Pace Instruction: Improving Student
Success by Integrating Content Mastery and Amplified Assistance.” Journal of Asynchronous Learning
Networks 17, no. 1 (January 2013).
> Reddy, Diane M., Raymond Fleming, and Laura Pedrick. Increasing Student Success: Evaluating the
Effectiveness of U-Pace Instruction at UWM. SEI case study. Louisville, CO: ELI, September 2012.
Wake Forest University, BioBook
> Bennett, Kristin Redington, and Allen Daniel Johnson. “Using Multi-Node Tools for Student Success in Non-
Major Science Classes.” EDUCAUSE Quarterly (December 15, 2011).
Association for the Advancement of Computing in Education, simSchool
> Gibson, David. “Psychometric Considerations for Simulation-Based Assessments of Teaching.” Forthcoming.
Draft version.
> Kruse, Stacy, and David Gibson. “Next Generation Learning Challenge: Simulating Teaching.” EDUCAUSE
Quarterly (December 15, 2011).
Bryn Mawr College
> Spohrer, Jennifer. “Blended Learning in a Liberal Arts College Setting.” ELI Webinar, June 2, 2014.
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Challenges to Supporting Deeper Learning through Technology 13 | Implementation and Scaling 15 | Evidence of Learning and Course Completion 16
Conclusion 17 | Resources 18
ABOUT THE AUTHOR
Andrea Venezia is associate professor in the Public Policy and Administration Department and associate director of
the Institute for Higher Education Leadership & Policy at California State University, Sacramento. Her work focuses
on improving student readiness for, and success in, some form of postsecondary education, particularly for students
who are traditionally underserved. Before she joined Sacramento State, Venezia worked at WestEd and oversaw a
line of work focused on such issues as high school reform, state and federal policy with regard to college and career
readiness, and community college readiness and success. She has authored, co-authored, and co-edited numerous
reports, chapters, articles, and books, including Supporting the College Dream (Corwin Press, forthcoming) and
Minding the Gap: Why Integrating High School with College Makes Sense and How to Do It ( Harvard Education Press
2007). She received a BA in English from Pomona College, an MA in administration and policy analysis in higher
education from Stanford University, and a PhD in public policy from the Lyndon B. Johnson School of Public Affairs
at the University of Texas at Austin.
ACKNOWLEDGMENTS
Next Generation Learning Challenges would like to acknowledge with appreciation the contributions of Barbara
Means, Linda Sheer, Ying Zheng, Rebecca Deutscher, and others at SRI International who conducted the evaluation
of the Wave I: Building Blocks for College Completion grant projects; the principal investigators of all deeper learning
grant projects for their dedication to improving student outcomes and for sharing their experiences and lessons
learned, the partner institutions that worked with them, and the faculty and students who used the technology-
enabled innovations in their courses; and Cynthia Powell, Diane Reddy, and Jennifer Spohrer, who participated in
interviews with the author and provided valuable insight that greatly informed this report.
NEXT GENERATION LEARNING CHALLENGES
Next Generation Learning Challenges (NGLC) accelerates educational innovation through applied technology to
dramatically improve college readiness and completion in the United States. This multi year program provides
investment capital to expand the use of proven and emerging learning technologies, collects and shares evidence of
what works, and fosters innovation and adoption of solutions that will dramatically improve the quality of learning in
the United States, particularly for low-income students and students of color.
ORGANIZATIONAL PARTNERS AND FUNDERS
NGLC is a partnership led by EDUCAUSE and funded primarily by the Bill & Melinda Gates Foundation. Other
partners include the League for Innovation in the Community College, the International Association for K-12 Online
Learning (iNACOL), and the Council of Chief State School Officers (CCSSO).
The Potential: What Is Deeper Learning 2 | NGLC’s Deeper Learning Challenge 4 | NGLC-Funded Innovations 4 | Deeper Learning Approaches 5
Challenges to Supporting Deeper Learning through Technology 9 | Implementation and Scaling 11 | Evidence of Learning and Course Completion 14
Conclusion 15 | Resources 16
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